Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_arimabackwardselection.wasp
Title produced by softwareARIMA Backward Selection
Date of computationThu, 10 Dec 2009 14:55:51 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/10/t1260482271ff58hoyjzonao4h.htm/, Retrieved Fri, 29 Mar 2024 09:12:06 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65823, Retrieved Fri, 29 Mar 2024 09:12:06 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact162
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [ARIMA Backward Selection] [] [2009-11-27 14:53:14] [b98453cac15ba1066b407e146608df68]
-   PD    [ARIMA Backward Selection] [WS9: Backward Arima] [2009-12-04 19:42:32] [5c968c05ca472afa314d272082b56b09]
-   PD        [ARIMA Backward Selection] [WS10: Arima op Yt] [2009-12-10 21:55:51] [b8ce264f75295a954feffaf60221d1b0] [Current]
-    D          [ARIMA Backward Selection] [Workshop 10] [2009-12-11 20:24:43] [b6394cb5c2dcec6d17418d3cdf42d699]
-    D          [ARIMA Backward Selection] [WS 10(8) - Arima ...] [2009-12-11 21:18:57] [aba88da643e3763d32ff92bd8f92a385]
Feedback Forum

Post a new message
Dataseries X:
15,89
16,93
20,28
22,52
23,51
22,59
23,51
24,76
26,08
25,29
23,38
25,29
28,42
31,85
30,1
25,45
24,95
26,84
27,52
27,94
25,23
26,53
27,21
28,53
30,35
31,21
32,86
33,2
35,73
34,53
36,54
40,1
40,56
46,14
42,85
38,22
40,18
42,19
47,56
47,26
44,03
49,83
53,35
58,9
59,64
56,99
53,2
53,24
57,85
55,69
55,64
62,52
64,4
64,65
67,71
67,21
59,37
53,26
52,42
55,03




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 11 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65823&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65823&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65823&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.3397-0.2772-0.0791-0.02910.92180.0447-0.8932
(p-val)(0.5461 )(0.2316 )(0.7046 )(0.9581 )(0.0698 )(0.8363 )(0.2219 )
Estimates ( 2 )0.3114-0.2682-0.087300.9310.0419-0.9056
(p-val)(0.0218 )(0.0655 )(0.5253 )(NA )(0.0366 )(0.8443 )(0.174 )
Estimates ( 3 )0.304-0.2584-0.084301.30930-1.1897
(p-val)(0.026 )(0.0788 )(0.5388 )(NA )(0.1746 )(NA )(0.339 )
Estimates ( 4 )0.3388-0.3009000.98790-0.9442
(p-val)(0.0089 )(0.0235 )(NA )(NA )(0 )(NA )(0 )
Estimates ( 5 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ma1 & sar1 & sar2 & sma1 \tabularnewline
Estimates ( 1 ) & 0.3397 & -0.2772 & -0.0791 & -0.0291 & 0.9218 & 0.0447 & -0.8932 \tabularnewline
(p-val) & (0.5461 ) & (0.2316 ) & (0.7046 ) & (0.9581 ) & (0.0698 ) & (0.8363 ) & (0.2219 ) \tabularnewline
Estimates ( 2 ) & 0.3114 & -0.2682 & -0.0873 & 0 & 0.931 & 0.0419 & -0.9056 \tabularnewline
(p-val) & (0.0218 ) & (0.0655 ) & (0.5253 ) & (NA ) & (0.0366 ) & (0.8443 ) & (0.174 ) \tabularnewline
Estimates ( 3 ) & 0.304 & -0.2584 & -0.0843 & 0 & 1.3093 & 0 & -1.1897 \tabularnewline
(p-val) & (0.026 ) & (0.0788 ) & (0.5388 ) & (NA ) & (0.1746 ) & (NA ) & (0.339 ) \tabularnewline
Estimates ( 4 ) & 0.3388 & -0.3009 & 0 & 0 & 0.9879 & 0 & -0.9442 \tabularnewline
(p-val) & (0.0089 ) & (0.0235 ) & (NA ) & (NA ) & (0 ) & (NA ) & (0 ) \tabularnewline
Estimates ( 5 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 12 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 13 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65823&T=1

[TABLE]
[ROW][C]ARIMA Parameter Estimation and Backward Selection[/C][/ROW]
[ROW][C]Iteration[/C][C]ar1[/C][C]ar2[/C][C]ar3[/C][C]ma1[/C][C]sar1[/C][C]sar2[/C][C]sma1[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]0.3397[/C][C]-0.2772[/C][C]-0.0791[/C][C]-0.0291[/C][C]0.9218[/C][C]0.0447[/C][C]-0.8932[/C][/ROW]
[ROW][C](p-val)[/C][C](0.5461 )[/C][C](0.2316 )[/C][C](0.7046 )[/C][C](0.9581 )[/C][C](0.0698 )[/C][C](0.8363 )[/C][C](0.2219 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.3114[/C][C]-0.2682[/C][C]-0.0873[/C][C]0[/C][C]0.931[/C][C]0.0419[/C][C]-0.9056[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0218 )[/C][C](0.0655 )[/C][C](0.5253 )[/C][C](NA )[/C][C](0.0366 )[/C][C](0.8443 )[/C][C](0.174 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.304[/C][C]-0.2584[/C][C]-0.0843[/C][C]0[/C][C]1.3093[/C][C]0[/C][C]-1.1897[/C][/ROW]
[ROW][C](p-val)[/C][C](0.026 )[/C][C](0.0788 )[/C][C](0.5388 )[/C][C](NA )[/C][C](0.1746 )[/C][C](NA )[/C][C](0.339 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.3388[/C][C]-0.3009[/C][C]0[/C][C]0[/C][C]0.9879[/C][C]0[/C][C]-0.9442[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0089 )[/C][C](0.0235 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 8 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 9 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 10 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 11 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 12 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 13 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65823&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65823&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.3397-0.2772-0.0791-0.02910.92180.0447-0.8932
(p-val)(0.5461 )(0.2316 )(0.7046 )(0.9581 )(0.0698 )(0.8363 )(0.2219 )
Estimates ( 2 )0.3114-0.2682-0.087300.9310.0419-0.9056
(p-val)(0.0218 )(0.0655 )(0.5253 )(NA )(0.0366 )(0.8443 )(0.174 )
Estimates ( 3 )0.304-0.2584-0.084301.30930-1.1897
(p-val)(0.026 )(0.0788 )(0.5388 )(NA )(0.1746 )(NA )(0.339 )
Estimates ( 4 )0.3388-0.3009000.98790-0.9442
(p-val)(0.0089 )(0.0235 )(NA )(NA )(0 )(NA )(0 )
Estimates ( 5 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
0.0013185971719996
0.00781931065374887
0.0212512305223098
0.00874689910727674
0.00860164263138327
-0.00153141344774415
0.00997599534678595
0.00457807998043974
0.00592028615634509
-0.00428859080012960
-0.00700539473426023
0.0141994654523340
0.0102129042304227
0.0123724417911194
-0.0098096485711878
-0.0165453923441689
0.00288903617241458
0.0044638735506802
-0.00321328409298021
0.0030376988843857
-0.0139480026427060
0.0125252862777586
-0.00130548571092405
0.00549626471303142
0.0078256985614164
0.00127126411294119
0.00730065582943693
0.00128434855464649
0.0111661593689814
-0.00736820128400245
0.0117370597133220
0.00993155842978007
-0.000495312653262475
0.0221819751704306
-0.0141344471052687
-0.00993046565131243
0.0100256780233412
-0.00304464424002684
0.0134949274709352
-0.00368894697379976
-0.00765100460789797
0.0236068553297870
-0.000129442551100187
0.0142635770564559
0.00196992416000309
-0.0052682032397435
-0.00420712686293169
0.000352246938974349
0.00706555319660693
-0.0141812590009433
0.000574275251327509
0.0184648251529308
-0.00446535092197855
0.00336330878466315
0.00727043392735363
-0.00587533647308858
-0.0169857454087846
-0.0137977788199288
0.00108285271055416
0.000588874326545086

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.0013185971719996 \tabularnewline
0.00781931065374887 \tabularnewline
0.0212512305223098 \tabularnewline
0.00874689910727674 \tabularnewline
0.00860164263138327 \tabularnewline
-0.00153141344774415 \tabularnewline
0.00997599534678595 \tabularnewline
0.00457807998043974 \tabularnewline
0.00592028615634509 \tabularnewline
-0.00428859080012960 \tabularnewline
-0.00700539473426023 \tabularnewline
0.0141994654523340 \tabularnewline
0.0102129042304227 \tabularnewline
0.0123724417911194 \tabularnewline
-0.0098096485711878 \tabularnewline
-0.0165453923441689 \tabularnewline
0.00288903617241458 \tabularnewline
0.0044638735506802 \tabularnewline
-0.00321328409298021 \tabularnewline
0.0030376988843857 \tabularnewline
-0.0139480026427060 \tabularnewline
0.0125252862777586 \tabularnewline
-0.00130548571092405 \tabularnewline
0.00549626471303142 \tabularnewline
0.0078256985614164 \tabularnewline
0.00127126411294119 \tabularnewline
0.00730065582943693 \tabularnewline
0.00128434855464649 \tabularnewline
0.0111661593689814 \tabularnewline
-0.00736820128400245 \tabularnewline
0.0117370597133220 \tabularnewline
0.00993155842978007 \tabularnewline
-0.000495312653262475 \tabularnewline
0.0221819751704306 \tabularnewline
-0.0141344471052687 \tabularnewline
-0.00993046565131243 \tabularnewline
0.0100256780233412 \tabularnewline
-0.00304464424002684 \tabularnewline
0.0134949274709352 \tabularnewline
-0.00368894697379976 \tabularnewline
-0.00765100460789797 \tabularnewline
0.0236068553297870 \tabularnewline
-0.000129442551100187 \tabularnewline
0.0142635770564559 \tabularnewline
0.00196992416000309 \tabularnewline
-0.0052682032397435 \tabularnewline
-0.00420712686293169 \tabularnewline
0.000352246938974349 \tabularnewline
0.00706555319660693 \tabularnewline
-0.0141812590009433 \tabularnewline
0.000574275251327509 \tabularnewline
0.0184648251529308 \tabularnewline
-0.00446535092197855 \tabularnewline
0.00336330878466315 \tabularnewline
0.00727043392735363 \tabularnewline
-0.00587533647308858 \tabularnewline
-0.0169857454087846 \tabularnewline
-0.0137977788199288 \tabularnewline
0.00108285271055416 \tabularnewline
0.000588874326545086 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65823&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.0013185971719996[/C][/ROW]
[ROW][C]0.00781931065374887[/C][/ROW]
[ROW][C]0.0212512305223098[/C][/ROW]
[ROW][C]0.00874689910727674[/C][/ROW]
[ROW][C]0.00860164263138327[/C][/ROW]
[ROW][C]-0.00153141344774415[/C][/ROW]
[ROW][C]0.00997599534678595[/C][/ROW]
[ROW][C]0.00457807998043974[/C][/ROW]
[ROW][C]0.00592028615634509[/C][/ROW]
[ROW][C]-0.00428859080012960[/C][/ROW]
[ROW][C]-0.00700539473426023[/C][/ROW]
[ROW][C]0.0141994654523340[/C][/ROW]
[ROW][C]0.0102129042304227[/C][/ROW]
[ROW][C]0.0123724417911194[/C][/ROW]
[ROW][C]-0.0098096485711878[/C][/ROW]
[ROW][C]-0.0165453923441689[/C][/ROW]
[ROW][C]0.00288903617241458[/C][/ROW]
[ROW][C]0.0044638735506802[/C][/ROW]
[ROW][C]-0.00321328409298021[/C][/ROW]
[ROW][C]0.0030376988843857[/C][/ROW]
[ROW][C]-0.0139480026427060[/C][/ROW]
[ROW][C]0.0125252862777586[/C][/ROW]
[ROW][C]-0.00130548571092405[/C][/ROW]
[ROW][C]0.00549626471303142[/C][/ROW]
[ROW][C]0.0078256985614164[/C][/ROW]
[ROW][C]0.00127126411294119[/C][/ROW]
[ROW][C]0.00730065582943693[/C][/ROW]
[ROW][C]0.00128434855464649[/C][/ROW]
[ROW][C]0.0111661593689814[/C][/ROW]
[ROW][C]-0.00736820128400245[/C][/ROW]
[ROW][C]0.0117370597133220[/C][/ROW]
[ROW][C]0.00993155842978007[/C][/ROW]
[ROW][C]-0.000495312653262475[/C][/ROW]
[ROW][C]0.0221819751704306[/C][/ROW]
[ROW][C]-0.0141344471052687[/C][/ROW]
[ROW][C]-0.00993046565131243[/C][/ROW]
[ROW][C]0.0100256780233412[/C][/ROW]
[ROW][C]-0.00304464424002684[/C][/ROW]
[ROW][C]0.0134949274709352[/C][/ROW]
[ROW][C]-0.00368894697379976[/C][/ROW]
[ROW][C]-0.00765100460789797[/C][/ROW]
[ROW][C]0.0236068553297870[/C][/ROW]
[ROW][C]-0.000129442551100187[/C][/ROW]
[ROW][C]0.0142635770564559[/C][/ROW]
[ROW][C]0.00196992416000309[/C][/ROW]
[ROW][C]-0.0052682032397435[/C][/ROW]
[ROW][C]-0.00420712686293169[/C][/ROW]
[ROW][C]0.000352246938974349[/C][/ROW]
[ROW][C]0.00706555319660693[/C][/ROW]
[ROW][C]-0.0141812590009433[/C][/ROW]
[ROW][C]0.000574275251327509[/C][/ROW]
[ROW][C]0.0184648251529308[/C][/ROW]
[ROW][C]-0.00446535092197855[/C][/ROW]
[ROW][C]0.00336330878466315[/C][/ROW]
[ROW][C]0.00727043392735363[/C][/ROW]
[ROW][C]-0.00587533647308858[/C][/ROW]
[ROW][C]-0.0169857454087846[/C][/ROW]
[ROW][C]-0.0137977788199288[/C][/ROW]
[ROW][C]0.00108285271055416[/C][/ROW]
[ROW][C]0.000588874326545086[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65823&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65823&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Estimated ARIMA Residuals
Value
0.0013185971719996
0.00781931065374887
0.0212512305223098
0.00874689910727674
0.00860164263138327
-0.00153141344774415
0.00997599534678595
0.00457807998043974
0.00592028615634509
-0.00428859080012960
-0.00700539473426023
0.0141994654523340
0.0102129042304227
0.0123724417911194
-0.0098096485711878
-0.0165453923441689
0.00288903617241458
0.0044638735506802
-0.00321328409298021
0.0030376988843857
-0.0139480026427060
0.0125252862777586
-0.00130548571092405
0.00549626471303142
0.0078256985614164
0.00127126411294119
0.00730065582943693
0.00128434855464649
0.0111661593689814
-0.00736820128400245
0.0117370597133220
0.00993155842978007
-0.000495312653262475
0.0221819751704306
-0.0141344471052687
-0.00993046565131243
0.0100256780233412
-0.00304464424002684
0.0134949274709352
-0.00368894697379976
-0.00765100460789797
0.0236068553297870
-0.000129442551100187
0.0142635770564559
0.00196992416000309
-0.0052682032397435
-0.00420712686293169
0.000352246938974349
0.00706555319660693
-0.0141812590009433
0.000574275251327509
0.0184648251529308
-0.00446535092197855
0.00336330878466315
0.00727043392735363
-0.00587533647308858
-0.0169857454087846
-0.0137977788199288
0.00108285271055416
0.000588874326545086



Parameters (Session):
par1 = FALSE ; par2 = 0.1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 0.1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
R code (references can be found in the software module):
library(lattice)
if (par1 == 'TRUE') par1 <- TRUE
if (par1 == 'FALSE') par1 <- FALSE
par2 <- as.numeric(par2) #Box-Cox lambda transformation parameter
par3 <- as.numeric(par3) #degree of non-seasonal differencing
par4 <- as.numeric(par4) #degree of seasonal differencing
par5 <- as.numeric(par5) #seasonal period
par6 <- as.numeric(par6) #degree (p) of the non-seasonal AR(p) polynomial
par7 <- as.numeric(par7) #degree (q) of the non-seasonal MA(q) polynomial
par8 <- as.numeric(par8) #degree (P) of the seasonal AR(P) polynomial
par9 <- as.numeric(par9) #degree (Q) of the seasonal MA(Q) polynomial
armaGR <- function(arima.out, names, n){
try1 <- arima.out$coef
try2 <- sqrt(diag(arima.out$var.coef))
try.data.frame <- data.frame(matrix(NA,ncol=4,nrow=length(names)))
dimnames(try.data.frame) <- list(names,c('coef','std','tstat','pv'))
try.data.frame[,1] <- try1
for(i in 1:length(try2)) try.data.frame[which(rownames(try.data.frame)==names(try2)[i]),2] <- try2[i]
try.data.frame[,3] <- try.data.frame[,1] / try.data.frame[,2]
try.data.frame[,4] <- round((1-pt(abs(try.data.frame[,3]),df=n-(length(try2)+1)))*2,5)
vector <- rep(NA,length(names))
vector[is.na(try.data.frame[,4])] <- 0
maxi <- which.max(try.data.frame[,4])
continue <- max(try.data.frame[,4],na.rm=TRUE) > .05
vector[maxi] <- 0
list(summary=try.data.frame,next.vector=vector,continue=continue)
}
arimaSelect <- function(series, order=c(13,0,0), seasonal=list(order=c(2,0,0),period=12), include.mean=F){
nrc <- order[1]+order[3]+seasonal$order[1]+seasonal$order[3]
coeff <- matrix(NA, nrow=nrc*2, ncol=nrc)
pval <- matrix(NA, nrow=nrc*2, ncol=nrc)
mylist <- rep(list(NULL), nrc)
names <- NULL
if(order[1] > 0) names <- paste('ar',1:order[1],sep='')
if(order[3] > 0) names <- c( names , paste('ma',1:order[3],sep='') )
if(seasonal$order[1] > 0) names <- c(names, paste('sar',1:seasonal$order[1],sep=''))
if(seasonal$order[3] > 0) names <- c(names, paste('sma',1:seasonal$order[3],sep=''))
arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML')
mylist[[1]] <- arima.out
last.arma <- armaGR(arima.out, names, length(series))
mystop <- FALSE
i <- 1
coeff[i,] <- last.arma[[1]][,1]
pval [i,] <- last.arma[[1]][,4]
i <- 2
aic <- arima.out$aic
while(!mystop){
mylist[[i]] <- arima.out
arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML', fixed=last.arma$next.vector)
aic <- c(aic, arima.out$aic)
last.arma <- armaGR(arima.out, names, length(series))
mystop <- !last.arma$continue
coeff[i,] <- last.arma[[1]][,1]
pval [i,] <- last.arma[[1]][,4]
i <- i+1
}
list(coeff, pval, mylist, aic=aic)
}
arimaSelectplot <- function(arimaSelect.out,noms,choix){
noms <- names(arimaSelect.out[[3]][[1]]$coef)
coeff <- arimaSelect.out[[1]]
k <- min(which(is.na(coeff[,1])))-1
coeff <- coeff[1:k,]
pval <- arimaSelect.out[[2]][1:k,]
aic <- arimaSelect.out$aic[1:k]
coeff[coeff==0] <- NA
n <- ncol(coeff)
if(missing(choix)) choix <- k
layout(matrix(c(1,1,1,2,
3,3,3,2,
3,3,3,4,
5,6,7,7),nr=4),
widths=c(10,35,45,15),
heights=c(30,30,15,15))
couleurs <- rainbow(75)[1:50]#(50)
ticks <- pretty(coeff)
par(mar=c(1,1,3,1))
plot(aic,k:1-.5,type='o',pch=21,bg='blue',cex=2,axes=F,lty=2,xpd=NA)
points(aic[choix],k-choix+.5,pch=21,cex=4,bg=2,xpd=NA)
title('aic',line=2)
par(mar=c(3,0,0,0))
plot(0,axes=F,xlab='',ylab='',xlim=range(ticks),ylim=c(.1,1))
rect(xleft = min(ticks) + (0:49)/50*(max(ticks)-min(ticks)),
xright = min(ticks) + (1:50)/50*(max(ticks)-min(ticks)),
ytop = rep(1,50),
ybottom= rep(0,50),col=couleurs,border=NA)
axis(1,ticks)
rect(xleft=min(ticks),xright=max(ticks),ytop=1,ybottom=0)
text(mean(coeff,na.rm=T),.5,'coefficients',cex=2,font=2)
par(mar=c(1,1,3,1))
image(1:n,1:k,t(coeff[k:1,]),axes=F,col=couleurs,zlim=range(ticks))
for(i in 1:n) for(j in 1:k) if(!is.na(coeff[j,i])) {
if(pval[j,i]<.01) symb = 'green'
else if( (pval[j,i]<.05) & (pval[j,i]>=.01)) symb = 'orange'
else if( (pval[j,i]<.1) & (pval[j,i]>=.05)) symb = 'red'
else symb = 'black'
polygon(c(i+.5 ,i+.2 ,i+.5 ,i+.5),
c(k-j+0.5,k-j+0.5,k-j+0.8,k-j+0.5),
col=symb)
if(j==choix) {
rect(xleft=i-.5,
xright=i+.5,
ybottom=k-j+1.5,
ytop=k-j+.5,
lwd=4)
text(i,
k-j+1,
round(coeff[j,i],2),
cex=1.2,
font=2)
}
else{
rect(xleft=i-.5,xright=i+.5,ybottom=k-j+1.5,ytop=k-j+.5)
text(i,k-j+1,round(coeff[j,i],2),cex=1.2,font=1)
}
}
axis(3,1:n,noms)
par(mar=c(0.5,0,0,0.5))
plot(0,axes=F,xlab='',ylab='',type='n',xlim=c(0,8),ylim=c(-.2,.8))
cols <- c('green','orange','red','black')
niv <- c('0','0.01','0.05','0.1')
for(i in 0:3){
polygon(c(1+2*i ,1+2*i ,1+2*i-.5 ,1+2*i),
c(.4 ,.7 , .4 , .4),
col=cols[i+1])
text(2*i,0.5,niv[i+1],cex=1.5)
}
text(8,.5,1,cex=1.5)
text(4,0,'p-value',cex=2)
box()
residus <- arimaSelect.out[[3]][[choix]]$res
par(mar=c(1,2,4,1))
acf(residus,main='')
title('acf',line=.5)
par(mar=c(1,2,4,1))
pacf(residus,main='')
title('pacf',line=.5)
par(mar=c(2,2,4,1))
qqnorm(residus,main='')
title('qq-norm',line=.5)
qqline(residus)
residus
}
if (par2 == 0) x <- log(x)
if (par2 != 0) x <- x^par2
(selection <- arimaSelect(x, order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5)))
bitmap(file='test1.png')
resid <- arimaSelectplot(selection)
dev.off()
resid
bitmap(file='test2.png')
acf(resid,length(resid)/2, main='Residual Autocorrelation Function')
dev.off()
bitmap(file='test3.png')
pacf(resid,length(resid)/2, main='Residual Partial Autocorrelation Function')
dev.off()
bitmap(file='test4.png')
cpgram(resid, main='Residual Cumulative Periodogram')
dev.off()
bitmap(file='test5.png')
hist(resid, main='Residual Histogram', xlab='values of Residuals')
dev.off()
bitmap(file='test6.png')
densityplot(~resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test7.png')
qqnorm(resid, main='Residual Normal Q-Q Plot')
qqline(resid)
dev.off()
ncols <- length(selection[[1]][1,])
nrows <- length(selection[[2]][,1])-1
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ARIMA Parameter Estimation and Backward Selection', ncols+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Iteration', header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,names(selection[[3]][[1]]$coef)[i],header=TRUE)
}
a<-table.row.end(a)
for (j in 1:nrows) {
a<-table.row.start(a)
mydum <- 'Estimates ('
mydum <- paste(mydum,j)
mydum <- paste(mydum,')')
a<-table.element(a,mydum, header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,round(selection[[1]][j,i],4))
}
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'(p-val)', header=TRUE)
for (i in 1:ncols) {
mydum <- '('
mydum <- paste(mydum,round(selection[[2]][j,i],4),sep='')
mydum <- paste(mydum,')')
a<-table.element(a,mydum)
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimated ARIMA Residuals', 1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Value', 1,TRUE)
a<-table.row.end(a)
for (i in (par4*par5+par3):length(resid)) {
a<-table.row.start(a)
a<-table.element(a,resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')